Addressing Misspecification

Addressing model misspecification—where the assumed model doesn't perfectly reflect reality—is a critical challenge across numerous machine learning and statistical inference domains. Current research focuses on developing robust methods, such as data-driven calibration techniques and algorithms that explicitly account for misspecification in the model's design, to improve the reliability and accuracy of inferences and predictions. This work is significant because it directly tackles a fundamental limitation of many models, leading to more trustworthy results in applications ranging from reinforcement learning and inverse reinforcement learning to causal inference and Bayesian learning.